1. Field of the Invention
The present invention relates to optical character recognition systems, and more particularly to facsimile communication systems which further include character recognition logic for interpreting typewritten text and reducing such to standard code.
2. Description of the Prior Art
Transmission of a facsimile signal is most often done by way of telephone lines and therefore in addition to the cost of the equipment must bear the pro-rated cost of any long distance communication. In order to reduce the cost of the communication network used, various compression techniques were devised in the past for compressing the facsimile data to the minimal amount of data necessary to convey a graphical image. Typically such compression was performed by way of compressing either the run lengths or the redundancy of black and white scan signals without regard to the particular character symbol which has been scanned. Concurrent with this development there were other parallel developments wherein optical character recognition systems were provided for use in automatic reading as an input to digital computers. While there are still many areas of possible improvement in the amount of compression that can take place in straightforward facsimile communication, the major areas of redundancy have been covered and most breakthroughs occurring now have less than a major impact on the total communication time. Since most documents transmitted in facsimile comprise typewritten text, character recognition appears to offer great promise in reducing transmission time. Thus, the most positive way of increasing communication speed appears to reside in encoding, or compressing by way of optical character recognition. This last approach has additional benefits when applied to facsimile systems since in the event that certain portions of the document cannot be recognized the system can revert to a straightforward facsimile mode. Since character recognition is thus backed up by the facsimile mode the recognition system does not require absolute accuracy.
In most applications printed text comprises the most significant content of the bulk of facsimile transmission. Printed text, however, has never been well standardized and there are presently various accepted type fonts used for depicting the characters. By prior work type fonts like the OCR-B font were developed, however, because of a persisting lack of universal acceptance, recognition of other fonts is still desired and any automated recognition system should be amenable to easy modification to accommodate some of the other prevailing fonts.
Superposed over the problem of various type fonts is the variable fidelity of each particular printing machine or typewriter with which a particular type font is inscribed. For example, where printed matter is generated by a conventional typewriter, there exists over and above the variations in the font style the variation due to manufacturing tolerances of the font character itself, ribbon ink accumulation in the spaces within the character, the quality of the paper, dust or existing markings on the paper and the fidelity with which the character is transferred onto the paper. These latter variations are often referred to as "print quality".
Most prior art character recognition systems utilize what is in effect a character template, such templates being formed by digital code, and when a particular digitized scan of the character matches the template a character recognition is indicated. This template approach, however, requires extensive data point testing and furthermore lacks the desired flexibility to accommodate the above-described problems of print quality. For that reason various statistical correlation techniques have been attempted in the past for effecting an enhancement of the signal-to-noise features of the character itself. Further work in such template correlation has led to the conclusion that many characters, if not all the characters, in any alphabet, printed in all of the major fonts presently in use do not require a full template reading in order to obtain a match. Specifically, it has been found that only certain areas of a character need be positively identified in order to arrive at a conclusion as to the identity of the character.
Identification of such selected recognition features described above is typically performed in terms of the basic black-white distinction of a facsimile system. Since a facsimile system is normally limited in resolution by both the scanning raster and the digitized increments along the scan, commonly referred to as picture cells or "pixels", the alignment of the scanning raster or pixels relative the character introduces another source of error. This is commonly referred to as the "registration error".
Most prior art systems using such above-described pattern recognition techniques, in order to avoid such registration error, selected character pattern segments well centered within the character envelope as the character indices for identification. This immediately produced an additional problem in that certain smaller areas which distinguish similarly appearing characters are lost by way of these pattern recognition techniques. For example, the detail between the numeral "1" and the alphabetic symbol "I" appears most often in the upper right hand corner of the character "I" as a small projection. Loss of this data by use of only well-centered character identification points leads to confusion between the numeric "1" and the alphabetic "I". Similarly, the numeral "2" can be easily confused with the letter "Z" since in certain type fonts only the upper right hand curvature of "2" distinguishes such from the letter "Z". Again, this segment of the character in many type fonts, appears as a thin segment and therefore either the scanning raster density has to be increased or a confusing output has to be accepted.
Scanning raster or pixel density are normally a major cost item in any facsimile system, and furthermore typical scanning systems have already been devised with element density selected on the basis of character pitch and font height. Any character recognition system, in order to obtain best cost economics, should utilize such standardized scanners and is therefore limited to this detail density. Furthermore an increase in scanning density adds costs beyond those effected by standardization since both the data rate and volume have to increase.